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In the rush to dominate video, many analysts predicted the death of audio. Instead, we have seen a renaissance. Podcasts have become the dark horse of entertainment and media content. Why? Because audio is the only medium that is truly "second screen" compatible. Driven by convenience, curated curation, and shared cultural
Look at the trajectory of a show like Saturday Night Live or The Tonight Show . They no longer compete with YouTube; they borrow from it. They bring on TikTok singers, streamers, and viral comedians to tap into pre-existing fan bases. Meanwhile, studios are greenlighting movies based on Reddit horror stories (The Slenderman phenomenon) or Wattpad fanfictions ( After ). The pipeline of entertainment and media content now flows directly through the hands of the amateur. Podcasts have become the dark horse of entertainment
: Roughly 89% of audiences engage with content specifically suggested by algorithms. The industry is moving toward "managed media sources" that provide better device control and reduced latency for real-time streaming.
: Content was created for broad, generalized demographics to maximize advertising revenue. The Digital and Streaming Era
Recommendation engines use machine learning to analyze user behavior, watch history, and search patterns. This data creates a highly customized content feed for every individual. Beyond curation, generative AI is actively altering the production process, assisting in scriptwriting, video editing, visual effects, and automated language dubbing. The Power of Short-Form Video


4 comentarios
Buenas!
Muy interesante, alguna recomendación en castellano?
José Pena 29 de diciembre de 2021, 18:27
Hola José, sin dudas te recomiendo la traducción al español de «R for Data Science»: https://es.r4ds.hadley.nz/
Y en este post comparto más material en español que te puede interesar https://www.maximaformacion.es/blog-dat/estadistica-r-libros-y-hojas-de-referencia-en-espanol/
Un saludo!
Rosana Ferrero 17 de enero de 2022, 09:01
Me parece que os falta uno de los esenciales (a mi modo de parecer): R for Data Science, de Hadley Wickham.
Sergio Ciordia 2 de enero de 2022, 10:31
Tienes toda la razón Sergio, gracias por tu comentario, lo he agregado en primer lugar! Este post es un tanto antiguo y faltaba este libro que es un 10.
Un saludo y buen comienzo de semana
Rosana Ferrero 17 de enero de 2022, 08:58